This paper designs a safe, low-cost, and efficient permanent magnet synchronous motor (PMSM) booster pump system. The aim is to enhance the pump’s safety and reduce the incidence of electric shock accidents, while also achieving cost reduction and efficiency improvement. The pump components are made of a plastic material, and a safe voltage of 36 V is used as the operating voltage. Additionally, the PMSM is chosen to replace the induction motor (IM) as the pump’s driving device, utilizing sensorless control and field-weakening control strategies. The study results show that when the flow rate is 1.51 m³/h, the efficiency of the PMSM low-voltage pump can reach up to 20.86%. At the same flow rate of 1 m³/h, compared to other pumps, the PMSM low-voltage pump exhibits higher head, energy savings, and efficiency. The proposed PMSM low-voltage pump offers advantages such as high efficiency, energy savings, safety, and low cost. This study provides a reference for the domestic PMSM pump industry.

Single-pixel imaging enables two-dimensional image capture through a single-pixel detector, yet extending this to three-dimensional or higher-dimensional information capture in single-pixel optical imaging systems has remained a challenging problem. In this study, we present a single-pixel camera system for three-dimensional (3D) imaging based on compressed sensing (CS) with continuous wave (CW) pseudo-random volume structured illumination. An estimated image, which incorporates both spatial and depth information of a 3D scene, is reconstructed using an L1-norm minimization reconstruction algorithm. This algorithm employs prior knowledge of non-overlapping objects as a constraint in the target space, resulting in improved noise performance in both numerical simulations and physical experiments. Our simulations and experiments demonstrate the feasibility of the proposed 3D CS framework. This approach achieves compressive sensing in a 3D information capture system with a measurement ratio of 19.53%. Additionally, we show that our CS 3D capturing system can accurately reconstruct the color of a target using color filter modulation.

The measurement of ultra-intensive transient electric fields is essential for analyzing many extreme physical phenomena. However, it is still challenging for conventional electronic systems to measure both the ultra-intensive and ultra-wideband electric fields. In this paper, we report an ultra-intensive and ultra-wideband electric field sensing system with a lithium niobate (LiNbO3, LN)-integrated electro-optic probe and an all-fiber optic demodulation module. This electro-optic probe can transform electric field variations into light polarization state changes, and an all-fiber optic demodulator converts this into intensity changes for photodetector detection. The sensing system is theoretically capable of having a 23 GHz bandwidth, a 65 dB dynamic range, and a maximum measurable field of 3.16 MV·m⁻¹. Experimental results show that the system exhibits a wideband response ranging from 50 Hz to 20 GHz, a maximum measurable electric field of 2.83 MV·m⁻¹, and a dynamic range of 63 dB, approaching the theoretical design limit. This system also achieves a sensitivity of 0.65 V/m·Hz^−1/2 and an electric field responsivity of 0.2975 mV/(kV·m⁻¹). Furthermore, the electro-optic probe has a compact structure with a size of 2 × 2 × 12 mm³, enabling stable and non-destructive measurement of applied electric fields. The system can be used in complex electromagnetic environments, such as lightning measurement.

Speech-driven gesture generation is an emerging domain within virtual human creation, where current methods predominantly utilize Transformer-based architectures that necessitate extensive memory and are characterized by slow inference speeds. In response to these limitations, we propose \textit{DiM-Gestures}, a novel end-to-end generative model crafted to create highly personalized 3D full-body gestures solely from raw speech audio, employing Mamba-based architectures. This model integrates a Mamba-based fuzzy feature extractor with a non-autoregressive Adaptive Layer Normalization (AdaLN) Mamba-2 diffusion architecture. The extractor, leveraging a Mamba framework and a WavLM pre-trained model, autonomously derives implicit, continuous fuzzy features, which are then unified into a singular latent feature. This feature is processed by the AdaLN Mamba-2, which implements a uniform conditional mechanism across all tokens to robustly model the interplay between the fuzzy features and the resultant gesture sequence. This innovative approach guarantees high fidelity in gesture-speech synchronization while maintaining the naturalness of the gestures. Employing a diffusion model for training and inference, our framework has undergone extensive subjective and objective evaluations on the ZEGGS and BEAT datasets. These assessments substantiate our model's enhanced performance relative to contemporary state-of-the-art methods, demonstrating competitive outcomes with the DiTs architecture (Persona-Gestors) while optimizing memory usage and accelerating inference speed.